Microsoft's translation tools have won an annual competition that aims to find the best machine learning translation services. The company's tech was the top-rated translation tool across eight of 19 different languages through a series of challenges.
Microsoft Research Asia's machine learning translation won with translation tasks for Chinese-English, English-Finnish, English-German, English-Lithuanian, French-German, German-English, German-French, and Russian-English.
The company also performed well in other areas, coming second in English-Kazakh, Finnish-English and Lithuanian-English.
Microsoft was competing in the 2019 fourth Conference on Machine Translation (WMT19), which is now in its 14th year. This annual competition gives researchers and tech companies an opportunity to show how their translation technology is progressing.
Nineteen categories were part of the Conference on Machine Translation this year, and Microsoft took part in eleven. The company won 8 categories and came second in the remaining three it competed in.
“This year, the Microsoft Research Asia team applied innovative algorithms to its system, which significantly improved the quality of the machine translation results,” said Tie-Yan Liu, assistant managing director of Microsoft Research Asia.
“These algorithms were used to improve the platform's learning mechanism, pre-training, network architecture optimization, data enhancement and other processes required so that the system can perform better.”
Microsoft has been amongst the leaders in machine translation for some time. Last year, the company became the first to provide a translation for Chinese news articles to English with a level of quality matching a human translator.
“The realm of machine translation will continue to evolve with better algorithms, data set and technology. However, much of our research today is really inspired by how we humans do things. Language is complex and nuanced, as people can use different words to express the exact same concept. Hence, developing multi-dimensional algorithms is important in evolving machine translation systems so that they can deliver better outcomes,” said Liu.
“Our achievement at WMT19 serves to the further development of the field, whereby we hope that machine translation can become better in the years to come.”